{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PPS2YSEIJYMKQRGT5GUEFGNJS2","short_pith_number":"pith:PPS2YSEI","schema_version":"1.0","canonical_sha256":"7be5ac48884e18a844d3e9a84299a99686edabde6ab3f53364c301db05657edf","source":{"kind":"arxiv","id":"2203.11163","version":2},"attestation_state":"computed","paper":{"title":"Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jheng-Hong Yang, Jimmy Lin, Wei Zhong, Yuqing Xie","submitted_at":"2022-03-21T17:41:54Z","abstract_excerpt":"With the recent success of dense retrieval methods based on bi-encoders, studies have applied this approach to various interesting downstream retrieval tasks with good efficiency and in-domain effectiveness. Recently, we have also seen the presence of dense retrieval models in Math Information Retrieval (MIR) tasks, but the most effective systems remain classic retrieval methods that consider hand-crafted structure features. In this work, we try to combine the best of both worlds:\\ a well-defined structure search method for effective formula search and efficient bi-encoder dense retrieval mode"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2203.11163","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-03-21T17:41:54Z","cross_cats_sorted":[],"title_canon_sha256":"acdac5781d02f0cf875d055292d74261d25abeb3db7e730eb886d08a58e3a6a1","abstract_canon_sha256":"397d6d5b1f4108f566594c3c137af5c9e53c8f61fc8fee7c936aebb0c1d1d94a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:05.115178Z","signature_b64":"STnkb3fqQcu9wejubiwYDEcQuP05y7+FvDMNXvidLg/G9cGInTYtJQjK5fxcwen6rto5B3Pf1fQyI91udi53Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7be5ac48884e18a844d3e9a84299a99686edabde6ab3f53364c301db05657edf","last_reissued_at":"2026-07-05T05:09:05.114798Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:05.114798Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jheng-Hong Yang, Jimmy Lin, Wei Zhong, Yuqing Xie","submitted_at":"2022-03-21T17:41:54Z","abstract_excerpt":"With the recent success of dense retrieval methods based on bi-encoders, studies have applied this approach to various interesting downstream retrieval tasks with good efficiency and in-domain effectiveness. Recently, we have also seen the presence of dense retrieval models in Math Information Retrieval (MIR) tasks, but the most effective systems remain classic retrieval methods that consider hand-crafted structure features. In this work, we try to combine the best of both worlds:\\ a well-defined structure search method for effective formula search and efficient bi-encoder dense retrieval mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.11163","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.11163/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2203.11163","created_at":"2026-07-05T05:09:05.114853+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.11163v2","created_at":"2026-07-05T05:09:05.114853+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.11163","created_at":"2026-07-05T05:09:05.114853+00:00"},{"alias_kind":"pith_short_12","alias_value":"PPS2YSEIJYMK","created_at":"2026-07-05T05:09:05.114853+00:00"},{"alias_kind":"pith_short_16","alias_value":"PPS2YSEIJYMKQRGT","created_at":"2026-07-05T05:09:05.114853+00:00"},{"alias_kind":"pith_short_8","alias_value":"PPS2YSEI","created_at":"2026-07-05T05:09:05.114853+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2","json":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2.json","graph_json":"https://pith.science/api/pith-number/PPS2YSEIJYMKQRGT5GUEFGNJS2/graph.json","events_json":"https://pith.science/api/pith-number/PPS2YSEIJYMKQRGT5GUEFGNJS2/events.json","paper":"https://pith.science/paper/PPS2YSEI"},"agent_actions":{"view_html":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2","download_json":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2.json","view_paper":"https://pith.science/paper/PPS2YSEI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.11163&json=true","fetch_graph":"https://pith.science/api/pith-number/PPS2YSEIJYMKQRGT5GUEFGNJS2/graph.json","fetch_events":"https://pith.science/api/pith-number/PPS2YSEIJYMKQRGT5GUEFGNJS2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2/action/storage_attestation","attest_author":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2/action/author_attestation","sign_citation":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2/action/citation_signature","submit_replication":"https://pith.science/pith/PPS2YSEIJYMKQRGT5GUEFGNJS2/action/replication_record"}},"created_at":"2026-07-05T05:09:05.114853+00:00","updated_at":"2026-07-05T05:09:05.114853+00:00"}